Overview

Dataset statistics

Number of variables25
Number of observations18448
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory3.5 MiB
Average record size in memory200.0 B

Variable types

Numeric23
Categorical2

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
price is highly correlated with num_bath and 2 other fieldsHigh correlation
num_bed is highly correlated with num_bath and 1 other fieldsHigh correlation
num_bath is highly correlated with price and 5 other fieldsHigh correlation
size_house is highly correlated with price and 3 other fieldsHigh correlation
size_lot is highly correlated with avg_size_neighbor_lotHigh correlation
num_floors is highly correlated with num_bath and 1 other fieldsHigh correlation
year_built is highly correlated with num_bath and 1 other fieldsHigh correlation
zip is highly correlated with longitude and 9 other fieldsHigh correlation
latitude is highly correlated with warehousedist and 5 other fieldsHigh correlation
longitude is highly correlated with zip and 9 other fieldsHigh correlation
avg_size_neighbor_houses is highly correlated with price and 2 other fieldsHigh correlation
avg_size_neighbor_lot is highly correlated with size_lotHigh correlation
schooldist is highly correlated with zip and 9 other fieldsHigh correlation
supermarketdist is highly correlated with zip and 9 other fieldsHigh correlation
warehousedist is highly correlated with zip and 10 other fieldsHigh correlation
churchdist is highly correlated with zip and 10 other fieldsHigh correlation
collegedist is highly correlated with zip and 10 other fieldsHigh correlation
hospitaldist is highly correlated with zip and 10 other fieldsHigh correlation
train_stationdist is highly correlated with zip and 10 other fieldsHigh correlation
universitydist is highly correlated with zip and 10 other fieldsHigh correlation
hangardist is highly correlated with zip and 9 other fieldsHigh correlation
price is highly correlated with num_bath and 2 other fieldsHigh correlation
num_bed is highly correlated with num_bath and 1 other fieldsHigh correlation
num_bath is highly correlated with price and 5 other fieldsHigh correlation
size_house is highly correlated with price and 3 other fieldsHigh correlation
size_lot is highly correlated with avg_size_neighbor_lotHigh correlation
num_floors is highly correlated with num_bathHigh correlation
year_built is highly correlated with num_bathHigh correlation
zip is highly correlated with longitude and 8 other fieldsHigh correlation
latitude is highly correlated with schooldist and 7 other fieldsHigh correlation
longitude is highly correlated with zip and 9 other fieldsHigh correlation
avg_size_neighbor_houses is highly correlated with price and 2 other fieldsHigh correlation
avg_size_neighbor_lot is highly correlated with size_lotHigh correlation
schooldist is highly correlated with zip and 10 other fieldsHigh correlation
supermarketdist is highly correlated with zip and 9 other fieldsHigh correlation
warehousedist is highly correlated with zip and 10 other fieldsHigh correlation
churchdist is highly correlated with zip and 10 other fieldsHigh correlation
collegedist is highly correlated with zip and 10 other fieldsHigh correlation
hospitaldist is highly correlated with zip and 10 other fieldsHigh correlation
train_stationdist is highly correlated with zip and 10 other fieldsHigh correlation
universitydist is highly correlated with zip and 10 other fieldsHigh correlation
hangardist is highly correlated with latitude and 9 other fieldsHigh correlation
num_bed is highly correlated with size_houseHigh correlation
num_bath is highly correlated with size_houseHigh correlation
size_house is highly correlated with num_bed and 2 other fieldsHigh correlation
size_lot is highly correlated with avg_size_neighbor_lotHigh correlation
latitude is highly correlated with collegedistHigh correlation
longitude is highly correlated with schooldist and 4 other fieldsHigh correlation
avg_size_neighbor_houses is highly correlated with size_houseHigh correlation
avg_size_neighbor_lot is highly correlated with size_lotHigh correlation
schooldist is highly correlated with longitude and 8 other fieldsHigh correlation
supermarketdist is highly correlated with longitude and 8 other fieldsHigh correlation
warehousedist is highly correlated with longitude and 8 other fieldsHigh correlation
churchdist is highly correlated with longitude and 8 other fieldsHigh correlation
collegedist is highly correlated with latitude and 8 other fieldsHigh correlation
hospitaldist is highly correlated with longitude and 8 other fieldsHigh correlation
train_stationdist is highly correlated with schooldist and 7 other fieldsHigh correlation
universitydist is highly correlated with schooldist and 7 other fieldsHigh correlation
hangardist is highly correlated with schooldist and 7 other fieldsHigh correlation
price is highly correlated with num_bath and 3 other fieldsHigh correlation
num_bed is highly correlated with num_bath and 1 other fieldsHigh correlation
num_bath is highly correlated with price and 5 other fieldsHigh correlation
size_house is highly correlated with price and 4 other fieldsHigh correlation
size_lot is highly correlated with avg_size_neighbor_lot and 1 other fieldsHigh correlation
num_floors is highly correlated with year_builtHigh correlation
condition is highly correlated with year_builtHigh correlation
size_basement is highly correlated with price and 2 other fieldsHigh correlation
year_built is highly correlated with num_bath and 8 other fieldsHigh correlation
zip is highly correlated with year_built and 11 other fieldsHigh correlation
latitude is highly correlated with zip and 9 other fieldsHigh correlation
longitude is highly correlated with year_built and 10 other fieldsHigh correlation
avg_size_neighbor_houses is highly correlated with price and 2 other fieldsHigh correlation
avg_size_neighbor_lot is highly correlated with size_lotHigh correlation
schooldist is highly correlated with zip and 10 other fieldsHigh correlation
supermarketdist is highly correlated with zip and 10 other fieldsHigh correlation
warehousedist is highly correlated with zip and 10 other fieldsHigh correlation
churchdist is highly correlated with zip and 10 other fieldsHigh correlation
collegedist is highly correlated with year_built and 11 other fieldsHigh correlation
hospitaldist is highly correlated with size_lot and 12 other fieldsHigh correlation
train_stationdist is highly correlated with year_built and 11 other fieldsHigh correlation
universitydist is highly correlated with year_built and 11 other fieldsHigh correlation
hangardist is highly correlated with zip and 10 other fieldsHigh correlation
size_basement has 11174 (60.6%) zeros Zeros
renovation_date has 17661 (95.7%) zeros Zeros

Reproduction

Analysis started2022-12-01 21:24:55.585522
Analysis finished2022-12-01 21:25:59.943220
Duration1 minute and 4.36 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3670
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean542362.3713
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:00.026239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1321837.5
median450000
Q3648000
95-th percentile1180000
Maximum7700000
Range7622000
Interquartile range (IQR)326162.5

Descriptive statistics

Standard deviation372013.519
Coefficient of variation (CV)0.6859132173
Kurtosis36.39093848
Mean542362.3713
Median Absolute Deviation (MAD)150000
Skewness4.115766647
Sum1.000550103 × 1010
Variance1.383940583 × 1011
MonotonicityNot monotonic
2022-12-01T16:26:00.149267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000145
 
0.8%
450000142
 
0.8%
550000134
 
0.7%
500000128
 
0.7%
425000127
 
0.7%
400000121
 
0.7%
325000120
 
0.7%
300000119
 
0.6%
375000115
 
0.6%
525000113
 
0.6%
Other values (3660)17184
93.1%
ValueCountFrequency (%)
780001
< 0.1%
800001
< 0.1%
810001
< 0.1%
825001
< 0.1%
830001
< 0.1%
840001
< 0.1%
850001
< 0.1%
865001
< 0.1%
890001
< 0.1%
899501
< 0.1%
ValueCountFrequency (%)
77000001
< 0.1%
70625001
< 0.1%
68850001
< 0.1%
55700001
< 0.1%
53500001
< 0.1%
53000001
< 0.1%
51108001
< 0.1%
46680001
< 0.1%
45000001
< 0.1%
44890001
< 0.1%

num_bed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.372614918
Minimum0
Maximum33
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:00.251289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9338924435
Coefficient of variation (CV)0.2769045581
Kurtosis56.20934093
Mean3.372614918
Median Absolute Deviation (MAD)1
Skewness2.216603139
Sum62218
Variance0.872155096
MonotonicityNot monotonic
2022-12-01T16:26:00.328307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
38403
45.5%
45863
31.8%
22358
 
12.8%
51361
 
7.4%
6238
 
1.3%
1157
 
0.9%
734
 
0.2%
012
 
0.1%
812
 
0.1%
96
 
< 0.1%
Other values (2)4
 
< 0.1%
ValueCountFrequency (%)
012
 
0.1%
1157
 
0.9%
22358
 
12.8%
38403
45.5%
45863
31.8%
51361
 
7.4%
6238
 
1.3%
734
 
0.2%
812
 
0.1%
96
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
103
 
< 0.1%
96
 
< 0.1%
812
 
0.1%
734
 
0.2%
6238
 
1.3%
51361
 
7.4%
45863
31.8%
38403
45.5%
22358
 
12.8%

num_bath
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.118888226
Minimum0
Maximum8
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:00.426328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7723841466
Coefficient of variation (CV)0.3645233085
Kurtosis1.443049749
Mean2.118888226
Median Absolute Deviation (MAD)0.5
Skewness0.5379165743
Sum39089.25
Variance0.5965772699
MonotonicityNot monotonic
2022-12-01T16:26:00.522350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.54607
25.0%
13278
17.8%
1.752576
14.0%
2.251764
 
9.6%
21642
 
8.9%
1.51232
 
6.7%
2.751012
 
5.5%
3648
 
3.5%
3.5618
 
3.3%
3.25514
 
2.8%
Other values (20)557
 
3.0%
ValueCountFrequency (%)
07
 
< 0.1%
0.53
 
< 0.1%
0.7557
 
0.3%
13278
17.8%
1.256
 
< 0.1%
1.51232
 
6.7%
1.752576
14.0%
21642
 
8.9%
2.251764
 
9.6%
2.54607
25.0%
ValueCountFrequency (%)
82
 
< 0.1%
7.751
 
< 0.1%
7.51
 
< 0.1%
6.752
 
< 0.1%
6.52
 
< 0.1%
6.252
 
< 0.1%
66
< 0.1%
5.754
 
< 0.1%
5.510
0.1%
5.2513
0.1%

size_house
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct956
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2083.940915
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:00.631375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11430
median1920
Q32560
95-th percentile3770
Maximum13540
Range13250
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation921.4162178
Coefficient of variation (CV)0.442150836
Kurtosis5.633591899
Mean2083.940915
Median Absolute Deviation (MAD)550
Skewness1.502606641
Sum38444542
Variance849007.8465
MonotonicityNot monotonic
2022-12-01T16:26:00.750401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440125
 
0.7%
1300119
 
0.6%
1820113
 
0.6%
1660111
 
0.6%
1800110
 
0.6%
1320108
 
0.6%
1400107
 
0.6%
1010106
 
0.6%
1250106
 
0.6%
1480106
 
0.6%
Other values (946)17337
94.0%
ValueCountFrequency (%)
2901
< 0.1%
3801
< 0.1%
3841
< 0.1%
3902
< 0.1%
4101
< 0.1%
4201
< 0.1%
4301
< 0.1%
4601
< 0.1%
4801
< 0.1%
4901
< 0.1%
ValueCountFrequency (%)
135401
< 0.1%
120501
< 0.1%
100401
< 0.1%
98901
< 0.1%
96401
< 0.1%
92001
< 0.1%
86701
< 0.1%
80201
< 0.1%
80101
< 0.1%
80001
< 0.1%

size_lot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8807
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15036.02407
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:00.868427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1824
Q15050
median7600.5
Q310625.25
95-th percentile42727.4
Maximum1651359
Range1650839
Interquartile range (IQR)5575.25

Descriptive statistics

Standard deviation41814.54897
Coefficient of variation (CV)2.780957837
Kurtosis299.5845778
Mean15036.02407
Median Absolute Deviation (MAD)2600.5
Skewness13.39725552
Sum277384572
Variance1748456505
MonotonicityNot monotonic
2022-12-01T16:26:00.986454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000309
 
1.7%
6000249
 
1.3%
4000218
 
1.2%
7200194
 
1.1%
7500100
 
0.5%
480099
 
0.5%
960096
 
0.5%
840092
 
0.5%
450091
 
0.5%
360087
 
0.5%
Other values (8797)16913
91.7%
ValueCountFrequency (%)
5201
< 0.1%
5721
< 0.1%
6001
< 0.1%
6091
< 0.1%
6351
< 0.1%
6492
< 0.1%
6751
< 0.1%
6761
< 0.1%
6811
< 0.1%
6831
< 0.1%
ValueCountFrequency (%)
16513591
< 0.1%
11647941
< 0.1%
10742181
< 0.1%
10240681
< 0.1%
9829981
< 0.1%
9822781
< 0.1%
9204231
< 0.1%
8816541
< 0.1%
8712001
< 0.1%
7156901
< 0.1%

num_floors
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494606461
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:01.083476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5408059205
Coefficient of variation (CV)0.3618383397
Kurtosis-0.4790163078
Mean1.494606461
Median Absolute Deviation (MAD)0.5
Skewness0.6188375561
Sum27572.5
Variance0.2924710437
MonotonicityNot monotonic
2022-12-01T16:26:01.159492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
19124
49.5%
27030
38.1%
1.51617
 
8.8%
3525
 
2.8%
2.5144
 
0.8%
3.58
 
< 0.1%
ValueCountFrequency (%)
19124
49.5%
1.51617
 
8.8%
27030
38.1%
2.5144
 
0.8%
3525
 
2.8%
3.58
 
< 0.1%
ValueCountFrequency (%)
3.58
 
< 0.1%
3525
 
2.8%
2.5144
 
0.8%
27030
38.1%
1.51617
 
8.8%
19124
49.5%

is_waterfront
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.2 KiB
0
18307 
1
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18448
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

Length

2022-12-01T16:26:01.243512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-01T16:26:01.330531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

Most occurring characters

ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18448
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common18448
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII18448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018307
99.2%
1141
 
0.8%

condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size144.2 KiB
3
11941 
4
4865 
5
1466 
2
 
150
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters18448
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

Length

2022-12-01T16:26:01.405548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-12-01T16:26:01.501571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

Most occurring characters

ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18448
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common18448
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
311941
64.7%
44865
26.4%
51466
 
7.9%
2150
 
0.8%
126
 
0.1%

size_basement
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct283
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.5714983
Minimum0
Maximum4820
Zeros11174
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:01.599591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3570
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)570

Descriptive statistics

Standard deviation443.6075028
Coefficient of variation (CV)1.511071427
Kurtosis2.755863337
Mean293.5714983
Median Absolute Deviation (MAD)0
Skewness1.570351748
Sum5415807
Variance196787.6165
MonotonicityNot monotonic
2022-12-01T16:26:01.715617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011174
60.6%
600190
 
1.0%
700182
 
1.0%
800180
 
1.0%
500174
 
0.9%
400164
 
0.9%
1000133
 
0.7%
900126
 
0.7%
300115
 
0.6%
53093
 
0.5%
Other values (273)5917
32.1%
ValueCountFrequency (%)
011174
60.6%
102
 
< 0.1%
404
 
< 0.1%
508
 
< 0.1%
609
 
< 0.1%
651
 
< 0.1%
707
 
< 0.1%
8017
 
0.1%
9020
 
0.1%
10037
 
0.2%
ValueCountFrequency (%)
48201
< 0.1%
41301
< 0.1%
35001
< 0.1%
34801
< 0.1%
30001
< 0.1%
28501
< 0.1%
28101
< 0.1%
27301
< 0.1%
27201
< 0.1%
26201
< 0.1%

year_built
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.001138
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:01.827642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.36161911
Coefficient of variation (CV)0.01489680475
Kurtosis-0.6556912762
Mean1971.001138
Median Absolute Deviation (MAD)23
Skewness-0.4721390602
Sum36361029
Variance862.104677
MonotonicityNot monotonic
2022-12-01T16:26:01.942669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014468
 
2.5%
2006380
 
2.1%
2004378
 
2.0%
2005372
 
2.0%
2003367
 
2.0%
1977359
 
1.9%
2007358
 
1.9%
1978330
 
1.8%
2008319
 
1.7%
1968315
 
1.7%
Other values (106)14802
80.2%
ValueCountFrequency (%)
190075
0.4%
190125
 
0.1%
190220
 
0.1%
190337
0.2%
190441
0.2%
190563
0.3%
190677
0.4%
190752
0.3%
190873
0.4%
190983
0.4%
ValueCountFrequency (%)
201534
 
0.2%
2014468
2.5%
2013169
 
0.9%
2012144
 
0.8%
2011109
 
0.6%
2010122
 
0.7%
2009197
1.1%
2008319
1.7%
2007358
1.9%
2006380
2.1%

renovation_date
Real number (ℝ≥0)

ZEROS

Distinct68
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.14500217
Minimum0
Maximum2015
Zeros17661
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:02.060695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation403.3712626
Coefficient of variation (CV)4.737462592
Kurtosis18.49656836
Mean85.14500217
Median Absolute Deviation (MAD)0
Skewness4.526923714
Sum1570755
Variance162708.3755
MonotonicityNot monotonic
2022-12-01T16:26:02.176720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017661
95.7%
201477
 
0.4%
200334
 
0.2%
201333
 
0.2%
200730
 
0.2%
200029
 
0.2%
200529
 
0.2%
199023
 
0.1%
200421
 
0.1%
200621
 
0.1%
Other values (58)490
 
2.7%
ValueCountFrequency (%)
017661
95.7%
19341
 
< 0.1%
19402
 
< 0.1%
19441
 
< 0.1%
19453
 
< 0.1%
19462
 
< 0.1%
19481
 
< 0.1%
19502
 
< 0.1%
19511
 
< 0.1%
19531
 
< 0.1%
ValueCountFrequency (%)
201515
 
0.1%
201477
0.4%
201333
0.2%
201210
 
0.1%
201113
 
0.1%
201017
 
0.1%
200917
 
0.1%
200816
 
0.1%
200730
 
0.2%
200621
 
0.1%

zip
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.92145
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:02.300749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.49744016
Coefficient of variation (CV)0.0005454585433
Kurtosis-0.8627090916
Mean98077.92145
Median Absolute Deviation (MAD)42
Skewness0.3999911363
Sum1809341495
Variance2861.976104
MonotonicityNot monotonic
2022-12-01T16:26:02.423776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103512
 
2.8%
98038504
 
2.7%
98115495
 
2.7%
98117478
 
2.6%
98034477
 
2.6%
98052475
 
2.6%
98042471
 
2.6%
98118445
 
2.4%
98023429
 
2.3%
98006424
 
2.3%
Other values (60)13738
74.5%
ValueCountFrequency (%)
98001313
1.7%
98002169
 
0.9%
98003241
1.3%
98004266
1.4%
98005143
 
0.8%
98006424
2.3%
98007127
 
0.7%
98008243
1.3%
9801084
 
0.5%
98011178
1.0%
ValueCountFrequency (%)
98199269
1.5%
98198231
1.3%
98188112
 
0.6%
98178224
1.2%
98177220
1.2%
98168232
1.3%
98166217
1.2%
98155376
2.0%
9814848
 
0.3%
98146249
1.3%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18324
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5600304
Minimum47.15593331
Maximum47.77762383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:02.549804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum47.15593331
5-th percentile47.30984567
Q147.47152712
median47.57159932
Q347.67791844
95-th percentile47.74987093
Maximum47.77762383
Range0.62169052
Interquartile range (IQR)0.20639132

Descriptive statistics

Standard deviation0.1385573676
Coefficient of variation (CV)0.002913315371
Kurtosis-0.6735891043
Mean47.5600304
Median Absolute Deviation (MAD)0.104722125
Skewness-0.4865787418
Sum877387.4407
Variance0.01919814412
MonotonicityNot monotonic
2022-12-01T16:26:02.659838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.504490083
 
< 0.1%
47.571987312
 
< 0.1%
47.684514472
 
< 0.1%
47.495984782
 
< 0.1%
47.733197252
 
< 0.1%
47.373123282
 
< 0.1%
47.649900892
 
< 0.1%
47.483956312
 
< 0.1%
47.494835672
 
< 0.1%
47.707640142
 
< 0.1%
Other values (18314)18427
99.9%
ValueCountFrequency (%)
47.155933311
< 0.1%
47.159327751
< 0.1%
47.162199541
< 0.1%
47.164674091
< 0.1%
47.177494911
< 0.1%
47.177576141
< 0.1%
47.177643051
< 0.1%
47.179450011
< 0.1%
47.1803181
< 0.1%
47.180819341
< 0.1%
ValueCountFrequency (%)
47.777623831
< 0.1%
47.777592091
< 0.1%
47.777476551
< 0.1%
47.777474551
< 0.1%
47.77745991
< 0.1%
47.777448841
< 0.1%
47.777209031
< 0.1%
47.777203121
< 0.1%
47.777139991
< 0.1%
47.777119751
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18287
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2144191
Minimum-122.5186481
Maximum-121.315254
Zeros0
Zeros (%)0.0%
Negative18448
Negative (%)100.0%
Memory size144.2 KiB
2022-12-01T16:26:02.772863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-122.5186481
5-th percentile-122.387008
Q1-122.3280844
median-122.2306878
Q3-122.1257329
95-th percentile-121.9809708
Maximum-121.315254
Range1.2033941
Interquartile range (IQR)0.20235155

Descriptive statistics

Standard deviation0.1399104894
Coefficient of variation (CV)-0.001144795274
Kurtosis0.8173737449
Mean-122.2144191
Median Absolute Deviation (MAD)0.10073825
Skewness0.8505957554
Sum-2254611.603
Variance0.01957494504
MonotonicityNot monotonic
2022-12-01T16:26:02.889889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.33011623
 
< 0.1%
-122.36423412
 
< 0.1%
-122.39076822
 
< 0.1%
-122.15315962
 
< 0.1%
-122.37909292
 
< 0.1%
-122.37925932
 
< 0.1%
-122.05641572
 
< 0.1%
-122.3555212
 
< 0.1%
-122.34858892
 
< 0.1%
-122.26890772
 
< 0.1%
Other values (18277)18427
99.9%
ValueCountFrequency (%)
-122.51864811
< 0.1%
-122.51479751
< 0.1%
-122.51399911
< 0.1%
-122.51123131
< 0.1%
-122.51122421
< 0.1%
-122.50908031
< 0.1%
-122.50898251
< 0.1%
-122.5065481
< 0.1%
-122.50618131
< 0.1%
-122.50517161
< 0.1%
ValueCountFrequency (%)
-121.3152541
< 0.1%
-121.31942651
< 0.1%
-121.32487711
< 0.1%
-121.35907031
< 0.1%
-121.36390051
< 0.1%
-121.36407351
< 0.1%
-121.40208951
< 0.1%
-121.40285151
< 0.1%
-121.41727211
< 0.1%
-121.47296711
< 0.1%

avg_size_neighbor_houses
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct734
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.306483
Minimum399
Maximum6110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:03.007916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32370
95-th percentile3300
Maximum6110
Range5711
Interquartile range (IQR)880

Descriptive statistics

Standard deviation686.1731244
Coefficient of variation (CV)0.3451043037
Kurtosis1.541837883
Mean1988.306483
Median Absolute Deviation (MAD)410
Skewness1.100529225
Sum36680278
Variance470833.5566
MonotonicityNot monotonic
2022-12-01T16:26:03.114931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1560171
 
0.9%
1540169
 
0.9%
1440166
 
0.9%
1500152
 
0.8%
1460151
 
0.8%
1480145
 
0.8%
1580142
 
0.8%
1720141
 
0.8%
1760141
 
0.8%
1680139
 
0.8%
Other values (724)16931
91.8%
ValueCountFrequency (%)
3991
 
< 0.1%
4602
 
< 0.1%
6202
 
< 0.1%
6701
 
< 0.1%
6902
 
< 0.1%
7001
 
< 0.1%
7102
 
< 0.1%
7202
 
< 0.1%
7407
< 0.1%
7503
< 0.1%
ValueCountFrequency (%)
61101
 
< 0.1%
57905
< 0.1%
56101
 
< 0.1%
56001
 
< 0.1%
55001
 
< 0.1%
53801
 
< 0.1%
53401
 
< 0.1%
52201
 
< 0.1%
52001
 
< 0.1%
51701
 
< 0.1%

avg_size_neighbor_lot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7865
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12571.59622
Minimum651
Maximum858132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:03.233966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2004.4
Q15100
median7611
Q310050
95-th percentile36565.25
Maximum858132
Range857481
Interquartile range (IQR)4950

Descriptive statistics

Standard deviation26329.26021
Coefficient of variation (CV)2.094345042
Kurtosis130.0213224
Mean12571.59622
Median Absolute Deviation (MAD)2491
Skewness9.060581717
Sum231920807
Variance693229943.2
MonotonicityNot monotonic
2022-12-01T16:26:03.345595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000375
 
2.0%
4000304
 
1.6%
6000241
 
1.3%
7200183
 
1.0%
7500128
 
0.7%
4800117
 
0.6%
360096
 
0.5%
840095
 
0.5%
450094
 
0.5%
510092
 
0.5%
Other values (7855)16723
90.6%
ValueCountFrequency (%)
6511
 
< 0.1%
6591
 
< 0.1%
7482
< 0.1%
7504
< 0.1%
7551
 
< 0.1%
7571
 
< 0.1%
7881
 
< 0.1%
8091
 
< 0.1%
8102
< 0.1%
8172
< 0.1%
ValueCountFrequency (%)
8581321
< 0.1%
5606171
< 0.1%
4382131
< 0.1%
4347281
< 0.1%
4255811
< 0.1%
4119621
< 0.1%
3920401
< 0.1%
3868121
< 0.1%
3802791
< 0.1%
3600001
< 0.1%

schooldist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3207
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.713536969
Minimum0.03
Maximum71.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:03.462622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.29
Q10.86
median6.91
Q316.9
95-th percentile26.5
Maximum71.25
Range71.22
Interquartile range (IQR)16.04

Descriptive statistics

Standard deviation9.495807697
Coefficient of variation (CV)0.9775849649
Kurtosis0.790883657
Mean9.713536969
Median Absolute Deviation (MAD)6.3
Skewness1.002206583
Sum179195.33
Variance90.17036382
MonotonicityNot monotonic
2022-12-01T16:26:03.574647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.594
 
0.5%
0.6193
 
0.5%
0.4791
 
0.5%
0.4690
 
0.5%
0.4885
 
0.5%
0.3983
 
0.4%
0.5983
 
0.4%
0.3682
 
0.4%
0.4181
 
0.4%
0.3879
 
0.4%
Other values (3197)17587
95.3%
ValueCountFrequency (%)
0.031
 
< 0.1%
0.053
 
< 0.1%
0.061
 
< 0.1%
0.075
 
< 0.1%
0.0815
0.1%
0.0914
0.1%
0.121
0.1%
0.1119
0.1%
0.1227
0.1%
0.1324
0.1%
ValueCountFrequency (%)
71.251
< 0.1%
70.951
< 0.1%
70.541
< 0.1%
67.951
< 0.1%
67.591
< 0.1%
67.581
< 0.1%
64.811
< 0.1%
64.741
< 0.1%
64.061
< 0.1%
60.261
< 0.1%

supermarketdist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3684
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.91556158
Minimum0.12
Maximum77.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:03.693673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile3.44
Q17.72
median14.85
Q322.86
95-th percentile32.51
Maximum77.77
Range77.65
Interquartile range (IQR)15.14

Descriptive statistics

Standard deviation9.725700391
Coefficient of variation (CV)0.6110811952
Kurtosis0.5914897208
Mean15.91556158
Median Absolute Deviation (MAD)7.46
Skewness0.7501496581
Sum293610.28
Variance94.5892481
MonotonicityNot monotonic
2022-12-01T16:26:03.809700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0120
 
0.1%
3.8618
 
0.1%
3.5518
 
0.1%
4.218
 
0.1%
4.2918
 
0.1%
4.4617
 
0.1%
18.2717
 
0.1%
17.0717
 
0.1%
4.7917
 
0.1%
4.0516
 
0.1%
Other values (3674)18272
99.0%
ValueCountFrequency (%)
0.121
< 0.1%
0.281
< 0.1%
0.291
< 0.1%
0.471
< 0.1%
0.521
< 0.1%
0.562
< 0.1%
0.571
< 0.1%
0.751
< 0.1%
0.781
< 0.1%
0.791
< 0.1%
ValueCountFrequency (%)
77.771
< 0.1%
77.511
< 0.1%
77.111
< 0.1%
74.481
< 0.1%
74.161
< 0.1%
74.121
< 0.1%
71.691
< 0.1%
71.571
< 0.1%
69.251
< 0.1%
67.41
< 0.1%

warehousedist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3388
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.58868441
Minimum0.05
Maximum73.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:03.931123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile1.13
Q13.21
median9.035
Q319.18
95-th percentile28.25
Maximum73.43
Range73.38
Interquartile range (IQR)15.97

Descriptive statistics

Standard deviation9.660199668
Coefficient of variation (CV)0.8335889844
Kurtosis0.6795075975
Mean11.58868441
Median Absolute Deviation (MAD)6.525
Skewness0.9501622572
Sum213788.05
Variance93.31945762
MonotonicityNot monotonic
2022-12-01T16:26:04.043159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5840
 
0.2%
2.6134
 
0.2%
2.530
 
0.2%
2.1729
 
0.2%
2.6229
 
0.2%
1.3628
 
0.2%
2.228
 
0.2%
1.6127
 
0.1%
3.4126
 
0.1%
1.5926
 
0.1%
Other values (3378)18151
98.4%
ValueCountFrequency (%)
0.051
 
< 0.1%
0.11
 
< 0.1%
0.121
 
< 0.1%
0.142
< 0.1%
0.151
 
< 0.1%
0.162
< 0.1%
0.181
 
< 0.1%
0.193
< 0.1%
0.212
< 0.1%
0.224
< 0.1%
ValueCountFrequency (%)
73.431
< 0.1%
73.121
< 0.1%
72.711
< 0.1%
70.161
< 0.1%
69.81
< 0.1%
69.781
< 0.1%
66.91
< 0.1%
66.851
< 0.1%
66.141
< 0.1%
62.51
< 0.1%

churchdist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3483
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.38343235
Minimum0.02
Maximum72.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:04.161186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.42
Q11.65
median8.6
Q319.1525
95-th percentile29.4765
Maximum72.83
Range72.81
Interquartile range (IQR)17.5025

Descriptive statistics

Standard deviation10.37529877
Coefficient of variation (CV)0.9114385231
Kurtosis0.273512567
Mean11.38343235
Median Absolute Deviation (MAD)7.49
Skewness0.8714400901
Sum210001.56
Variance107.6468246
MonotonicityNot monotonic
2022-12-01T16:26:04.270201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7453
 
0.3%
0.4153
 
0.3%
0.552
 
0.3%
0.452
 
0.3%
0.4750
 
0.3%
0.8948
 
0.3%
0.6246
 
0.2%
0.6345
 
0.2%
0.5645
 
0.2%
0.6945
 
0.2%
Other values (3473)17959
97.3%
ValueCountFrequency (%)
0.021
 
< 0.1%
0.031
 
< 0.1%
0.045
 
< 0.1%
0.058
< 0.1%
0.064
 
< 0.1%
0.0710
0.1%
0.0817
0.1%
0.0913
0.1%
0.19
< 0.1%
0.1116
0.1%
ValueCountFrequency (%)
72.831
< 0.1%
72.511
< 0.1%
72.111
< 0.1%
69.551
< 0.1%
69.191
< 0.1%
69.171
< 0.1%
66.311
< 0.1%
66.251
< 0.1%
65.381
< 0.1%
63.761
< 0.1%

collegedist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4218
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.76923786
Minimum0.24
Maximum76.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:04.388228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.24
5-th percentile2.17
Q16.92
median14.26
Q324.77
95-th percentile37.2165
Maximum76.31
Range76.07
Interquartile range (IQR)17.85

Descriptive statistics

Standard deviation11.88112174
Coefficient of variation (CV)0.7085069605
Kurtosis-0.1469904367
Mean16.76923786
Median Absolute Deviation (MAD)8.67
Skewness0.7183724368
Sum309358.9
Variance141.1610539
MonotonicityNot monotonic
2022-12-01T16:26:04.507255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5319
 
0.1%
4.2618
 
0.1%
4.3318
 
0.1%
3.9917
 
0.1%
4.2517
 
0.1%
4.0117
 
0.1%
2.8716
 
0.1%
2.9716
 
0.1%
3.3516
 
0.1%
4.0616
 
0.1%
Other values (4208)18278
99.1%
ValueCountFrequency (%)
0.242
< 0.1%
0.31
< 0.1%
0.311
< 0.1%
0.331
< 0.1%
0.341
< 0.1%
0.351
< 0.1%
0.381
< 0.1%
0.41
< 0.1%
0.411
< 0.1%
0.451
< 0.1%
ValueCountFrequency (%)
76.311
< 0.1%
76.021
< 0.1%
75.611
< 0.1%
72.991
< 0.1%
72.651
< 0.1%
72.631
< 0.1%
72.291
< 0.1%
71.041
< 0.1%
69.881
< 0.1%
69.821
< 0.1%

hospitaldist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3749
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.47892238
Minimum0.07
Maximum72.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:04.629282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile1.25
Q14.14
median10.68
Q320.88
95-th percentile32.33
Maximum72.73
Range72.66
Interquartile range (IQR)16.74

Descriptive statistics

Standard deviation10.74770795
Coefficient of variation (CV)0.7973714552
Kurtosis0.222142814
Mean13.47892238
Median Absolute Deviation (MAD)7.76
Skewness0.8550592604
Sum248659.16
Variance115.5132262
MonotonicityNot monotonic
2022-12-01T16:26:04.737315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6131
 
0.2%
2.6228
 
0.2%
2.3526
 
0.1%
1.8525
 
0.1%
1.9325
 
0.1%
2.6525
 
0.1%
1.3424
 
0.1%
1.8823
 
0.1%
2.2223
 
0.1%
1.7123
 
0.1%
Other values (3739)18195
98.6%
ValueCountFrequency (%)
0.071
 
< 0.1%
0.081
 
< 0.1%
0.11
 
< 0.1%
0.111
 
< 0.1%
0.132
< 0.1%
0.142
< 0.1%
0.152
< 0.1%
0.173
< 0.1%
0.182
< 0.1%
0.191
 
< 0.1%
ValueCountFrequency (%)
72.731
< 0.1%
72.431
< 0.1%
72.021
< 0.1%
69.421
< 0.1%
69.062
< 0.1%
68.531
< 0.1%
67.391
< 0.1%
66.311
< 0.1%
66.241
< 0.1%
61.611
< 0.1%

train_stationdist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3867
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.57107925
Minimum0.1
Maximum74.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:04.852340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.66
Q15.25
median11.74
Q322.07
95-th percentile33.54
Maximum74.54
Range74.44
Interquartile range (IQR)16.82

Descriptive statistics

Standard deviation11.00772986
Coefficient of variation (CV)0.7554505519
Kurtosis0.1070939973
Mean14.57107925
Median Absolute Deviation (MAD)7.79
Skewness0.8144706752
Sum268807.27
Variance121.1701167
MonotonicityNot monotonic
2022-12-01T16:26:04.965366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2526
 
0.1%
2.3720
 
0.1%
2.8820
 
0.1%
2.3320
 
0.1%
3.420
 
0.1%
2.0119
 
0.1%
2.1219
 
0.1%
2.8419
 
0.1%
1.7918
 
0.1%
3.118
 
0.1%
Other values (3857)18249
98.9%
ValueCountFrequency (%)
0.11
 
< 0.1%
0.151
 
< 0.1%
0.21
 
< 0.1%
0.243
< 0.1%
0.251
 
< 0.1%
0.264
< 0.1%
0.272
< 0.1%
0.282
< 0.1%
0.292
< 0.1%
0.321
 
< 0.1%
ValueCountFrequency (%)
74.541
< 0.1%
74.251
< 0.1%
73.841
< 0.1%
71.231
< 0.1%
70.872
< 0.1%
68.91
< 0.1%
68.871
< 0.1%
68.151
< 0.1%
68.071
< 0.1%
63.541
< 0.1%

universitydist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4013
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.22622615
Minimum0.12
Maximum73.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:05.087393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile1.43
Q15.9
median12.53
Q322.58
95-th percentile35.0365
Maximum73.53
Range73.41
Interquartile range (IQR)16.68

Descriptive statistics

Standard deviation11.2432905
Coefficient of variation (CV)0.7384160983
Kurtosis0.09924153784
Mean15.22622615
Median Absolute Deviation (MAD)7.83
Skewness0.8019840244
Sum280893.42
Variance126.4115814
MonotonicityNot monotonic
2022-12-01T16:26:05.201419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5522
 
0.1%
3.2320
 
0.1%
1.6820
 
0.1%
1.3520
 
0.1%
1.520
 
0.1%
3.9520
 
0.1%
1.3719
 
0.1%
4.6918
 
0.1%
2.9917
 
0.1%
1.417
 
0.1%
Other values (4003)18255
99.0%
ValueCountFrequency (%)
0.121
 
< 0.1%
0.131
 
< 0.1%
0.173
< 0.1%
0.182
< 0.1%
0.193
< 0.1%
0.22
< 0.1%
0.212
< 0.1%
0.222
< 0.1%
0.232
< 0.1%
0.252
< 0.1%
ValueCountFrequency (%)
73.531
< 0.1%
73.251
< 0.1%
72.841
< 0.1%
71.051
< 0.1%
70.221
< 0.1%
69.871
< 0.1%
69.861
< 0.1%
68.831
< 0.1%
67.211
< 0.1%
67.121
< 0.1%

hangardist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3572
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.37227179
Minimum0.08
Maximum71.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size144.2 KiB
2022-12-01T16:26:05.318437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile2.62
Q16.15
median10.21
Q319.3225
95-th percentile30.7665
Maximum71.04
Range70.96
Interquartile range (IQR)13.1725

Descriptive statistics

Standard deviation9.466188402
Coefficient of variation (CV)0.7078967994
Kurtosis1.061115416
Mean13.37227179
Median Absolute Deviation (MAD)5.25
Skewness1.105302022
Sum246691.67
Variance89.60872286
MonotonicityNot monotonic
2022-12-01T16:26:05.431462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1825
 
0.1%
5.8124
 
0.1%
5.4123
 
0.1%
5.3523
 
0.1%
5.8223
 
0.1%
7.4622
 
0.1%
7.2122
 
0.1%
6.2922
 
0.1%
5.6922
 
0.1%
6.0422
 
0.1%
Other values (3562)18220
98.8%
ValueCountFrequency (%)
0.081
< 0.1%
0.131
< 0.1%
0.141
< 0.1%
0.21
< 0.1%
0.211
< 0.1%
0.242
< 0.1%
0.251
< 0.1%
0.311
< 0.1%
0.322
< 0.1%
0.351
< 0.1%
ValueCountFrequency (%)
71.041
< 0.1%
70.741
< 0.1%
70.331
< 0.1%
67.741
< 0.1%
67.71
< 0.1%
67.381
< 0.1%
67.371
< 0.1%
66.031
< 0.1%
64.571
< 0.1%
64.511
< 0.1%

Interactions

2022-12-01T16:25:56.596471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:24:57.751007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.293576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:02.860152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.490741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.500371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:12.063946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:14.777554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.293401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:19.773669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.623980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:25.197905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:27.779613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:30.289807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:33.181668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:35.745012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:38.219566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:40.756223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:43.673386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:46.121986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:48.614414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:51.078233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.561791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.705495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:24:57.864032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.403602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:02.980179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.610769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.610396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:12.174970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:14.900348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.406430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:19.885694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.738008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:25.310931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:27.889638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:30.401832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:33.290692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:35.859036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:38.330591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:40.868248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:43.782412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:46.231009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:48.722441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:51.187258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.671815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.807517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:24:57.969055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.504624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:03.097206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.719793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.716419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:12.275993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:15.008373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.511452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:19.992719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.843377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:25.426957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:27.995661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:30.513856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:33.392714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:35.979064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:38.437614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:40.974271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:43.884438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:46.337042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:48.825467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:51.290282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.776839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.915542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:24:58.082081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.612648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:03.210230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.835819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.830445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:12.383016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:15.120399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.620478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:20.105743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.954402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-12-01T16:25:50.753154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.223714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.270397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:58.840982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.071527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:02.611097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.256690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.276322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:11.846896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:14.300446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.081345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:19.562622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.401925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:24.975856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:27.562565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:30.074758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:32.960618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:35.516960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:38.008519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:40.526083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:43.461337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:45.908938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:48.393360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:50.863181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.336740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.378421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:58.946005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:00.182552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:02.737124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:05.368715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:09.391346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:11.953920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:14.406470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:17.185371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:19.668646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:22.513953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:25.086880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:27.671589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:30.180782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:33.070643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:35.630986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:38.113542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:40.639109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:43.568361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:46.015961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:48.507388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:50.972208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:53.452766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-01T16:25:56.487446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-01T16:26:05.562500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-01T16:26:05.804554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-01T16:26:06.047609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-01T16:26:06.254655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-01T16:26:06.364672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-01T16:25:59.144050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-01T16:25:59.784193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

pricenum_bednum_bathsize_housesize_lotnum_floorsis_waterfrontconditionsize_basementyear_builtrenovation_dateziplatitudelongitudeavg_size_neighbor_housesavg_size_neighbor_lotschooldistsupermarketdistwarehousedistchurchdistcollegedisthospitaldisttrain_stationdistuniversitydisthangardist
022190031.00118056501.0030195509817847.511234-122.256775134056500.236.272.323.5512.706.878.6210.555.28
153800032.25257072422.003400195119919812547.721023-122.318862169076390.9112.041.460.212.581.802.086.515.64
218000021.00770100001.0030193309802847.737927-122.233196272080624.2616.104.824.588.607.958.109.426.11
360400043.00196050001.005910196509813647.520820-122.393185136050001.236.164.692.0311.957.808.938.476.04
451000032.00168080801.0030198709807447.616812-122.0449011800750317.5522.1820.4518.7020.8018.4719.6118.1817.88
5122500044.5054201019301.0031530200109805347.656118-122.005287476010193019.4425.9222.9221.5124.1620.7422.4421.3719.45
625750032.25171568192.0030199509800347.309720-122.3270492238681921.5926.4022.5525.8634.1428.1029.9930.9325.49
722950031.00178074701.003730196009814647.512294-122.336595178081131.744.043.284.8411.675.927.688.403.29
832300032.50189065602.0030200309803847.368407-122.0308182390757022.7729.5625.0126.4235.2530.0331.5933.7128.57
966250032.50356097961.0031700196509800747.600660-122.1452962210892510.7914.5112.8011.1113.3712.3011.8910.9811.64

Last rows

pricenum_bednum_bathsize_housesize_lotnum_floorsis_waterfrontconditionsize_basementyear_builtrenovation_dateziplatitudelongitudeavg_size_neighbor_housesavg_size_neighbor_lotschooldistsupermarketdistwarehousedistchurchdistcollegedisthospitaldisttrain_stationdistuniversitydisthangardist
1843822400031.751500119681.0030201409801047.309481-122.00214613201130328.8535.7431.0632.8141.8036.3738.0140.0634.77
1843950725032.50227055362.0030200309806547.538886-121.8812142270573128.5633.1330.7629.2934.2432.1532.2331.9931.21
1844042900032.00149011263.0030201409814447.569929-122.288021140012300.433.553.011.175.781.612.074.360.40
1844161068542.50252060232.0030201409805647.513674-122.167422252060236.8912.159.058.3516.3011.8513.0915.3511.17
18442100750043.50351072002.003910200909813647.553718-122.398209205062001.355.854.641.509.086.747.105.826.29
1844336000032.50153011313.0030200909810347.699285-122.346105153015091.439.462.260.120.911.371.414.896.37
1844440000042.50231058132.0030201409814647.510733-122.361867183072001.045.083.853.6612.176.968.508.734.58
1844540210120.75102013502.0030200909814447.594358-122.298654102020070.564.091.331.663.031.630.542.142.73
1844640000032.50160023882.0030200409802747.534499-122.0690871410128714.4619.0216.6615.1721.0818.1818.6419.2817.34
1844732500020.75102010762.0030200809814447.594059-122.298635102013570.594.111.341.703.051.660.522.162.70

Duplicate rows

Most frequently occurring

pricenum_bednum_bathsize_housesize_lotnum_floorsis_waterfrontconditionsize_basementyear_builtrenovation_dateziplatitudelongitudeavg_size_neighbor_housesavg_size_neighbor_lotschooldistsupermarketdistwarehousedistchurchdistcollegedisthospitaldisttrain_stationdistuniversitydisthangardist# duplicates
055000041.75241084472.004350193619809807447.649901-122.08826025201478913.3719.6617.3115.4517.9114.5616.2015.1113.522
155500032.50194032112.0030200909802747.564378-122.0932551880307813.3617.2615.8013.5618.1316.0716.0216.0815.072
258500032.50229050892.0030200109800647.544285-122.171537229079847.3111.269.507.4413.8710.4011.1212.579.602